class_docstr: A class for converting a dataframe and/or url to a vectorstore embedded with a given embeddings model
class_docstr_tok: ["A","class","for","converting","a","dataframe","and\/or","url","to","a","vectorstore","embedded","with","a","given","embeddings","model"]
docstr: Create DB from text content.
docstr_tok: ["Create","DB","from","text","content","."]
code: def create_db_from_texts(
      self, documents, embeddings_model
  ) -> VectorStore:
      """Create DB from text content."""
  
      texts = [doc.page_content for doc in documents]
      metadata = [doc.metadata for doc in documents]
  
      return self.vector_store.from_texts(
          texts=texts, embedding=embeddings_model, metadatas=metadata
      )
  
file_path: mindsdb/mindsdb/integrations/handlers/rag_handler/ingest.py
imports: import time
  from typing import List
  import pandas
  from langchain.schema import Document
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain.vectorstores import VectorStore
  from mindsdb.integrations.handlers.rag_handler.settings import PersistedVectorStoreSaver, PersistedVectorStoreSaverConfig, RAGBaseParameters, VectorStoreFactory, df_to_documents, get_chroma_client, load_embeddings_model, url_to_documents
  from mindsdb.utilities import log
label_desc: Use image node_id 4 for calling the RAGIngestor obj's underlying member method code with example usage: obj.create_db_from_texts(documents, embeddings_model) and returns: self